Spiking Neural Networks Enable Low-Latency Anomaly Detection at the Large Hadron Collider

The search for new physics at the Large Hadron Collider relies increasingly on identifying rare, unexpected events, and anomaly detection techniques offer a powerful approach to this challenge. Barry M. Dillon, Jim Harkin, and Aqib Javed, from Ulster University, and their colleagues investigate the potential of Spiking Neural Networks to improve this process. Their research focuses on building AutoEncoders using these networks, which are particularly well-suited for the demanding, real-time requirements of particle physics data analysis. The team demonstrates that these Spiking Neural Network AutoEncoders perform competitively with conventional methods in identifying anomalous signals within data from the CMS detector, offering a promising pathway towards capturing previously hidden phenomena and advancing our understanding of the universe.

A central challenge in particle physics is processing the massive datasets generated by experiments like MINERvA, demanding faster and more efficient analytical methods. Researchers are investigating SNNs, a biologically realistic type of neural network, as a potentially more energy-efficient and faster alternative to traditional DL, especially when implemented on specialized neuromorphic hardware. Researchers are also exploring the fundamental principles of SNNs, their biological inspiration, and methods for training them, including surrogate gradient learning. The research demonstrates that FPGA implementation offers a viable option for accelerating both DL and SNNs, and that using reduced precision, such as 8-bit or lower, can significantly improve performance and reduce energy consumption on FPGAs. A project called BitHEP is specifically focused on exploring the limits of low-precision ML in HEP. Conventional methods for searching for new physics typically focus on specific, pre-defined signatures, potentially overlooking unexpected phenomena, and often require substantial computational resources. This research addresses this limitation by utilizing SNNs, a type of neural network that mimics the way the brain processes information, offering advantages in speed and efficiency. The core of this innovative methodology lies in the unique operational characteristics of SNNs, which differ significantly from traditional neural networks.

Instead of transmitting information using continuous values, SNNs communicate via discrete “spikes,” mimicking neuronal activity in the brain, and naturally process information in distinct time steps. This spike-based communication inherently reduces computational demands and memory usage, making SNNs particularly well-suited for real-time applications and deployment on specialized hardware like Field-Programmable Gate Arrays (FPGAs). To further optimize performance for real-time analysis, the team employs techniques commonly used in the field of “FastML,” focusing on minimizing latency and maximizing throughput. This involves streamlining the network architecture and reducing the precision of the numerical values used, a process known as quantization, without significantly compromising accuracy. AutoEncoders, a type of neural network, are well-suited to anomaly detection, learning to reconstruct expected data and flagging anything unusual as a potential signal, but deploying them at the LHC’s high collision rate presents considerable challenges. The team’s work focuses on adapting SNNs, which transmit information using discrete spikes rather than continuous values, for anomaly detection at the LHC’s trigger level, the initial stage of data selection. This is particularly important because the LHC generates data at an extremely high rate, and only a small fraction can be recorded for detailed analysis.

SNNs are inherently suited to low-latency, low-memory operation, making them ideal for deployment on Field-Programmable Gate Arrays (FPGAs), specialized hardware used to accelerate computations. This is a significant achievement, as it shows that SNNs can achieve comparable accuracy without requiring the computational resources of traditional deep learning approaches. Furthermore, SNNs offer a pathway to even greater efficiency, potentially allowing for more complex anomaly detection algorithms to be deployed at the LHC’s trigger level, increasing the chances of discovering new physics beyond the Standard Model. Notably, the SNN-AE demonstrated greater robustness to variations in network setup and the size of the training dataset. The findings suggest that SNN-AEs represent a promising approach to identifying new physics at colliders, particularly in scenarios where low latency and efficient processing are crucial.